Characterizing and Understanding Distributed GNN Training on GPUs
This work tackles performance issues in distributed GNN training for large graphs, offering incremental insights for optimization.
The paper analyzed distributed graph neural network (GNN) training on GPUs to address performance bottlenecks, providing guidelines for software and hardware optimization.
Graph neural network (GNN) has been demonstrated to be a powerful model in many domains for its effectiveness in learning over graphs. To scale GNN training for large graphs, a widely adopted approach is distributed training which accelerates training using multiple computing nodes. Maximizing the performance is essential, but the execution of distributed GNN training remains preliminarily understood. In this work, we provide an in-depth analysis of distributed GNN training on GPUs, revealing several significant observations and providing useful guidelines for both software optimization and hardware optimization.